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DeepMind's Go-Playing AI Doesn't Need Human Help To Beat Us Anymore (theverge.com)
An anonymous reader quotes a report from The Verge: Google's AI subsidiary DeepMind has unveiled the latest version of its Go-playing software, AlphaGo Zero. The new program is a significantly better player than the version that beat the game's world champion earlier this year, but, more importantly, it's also entirely self-taught. DeepMind says this means the company is one step closer to creating general purpose algorithms that can intelligently tackle some of the hardest problems in science, from designing new drugs to more accurately modeling the effects of climate change. The original AlphaGo demonstrated superhuman Go-playing ability, but needed the expertise of human players to get there. Namely, it used a dataset of more than 100,000 Go games as a starting point for its own knowledge. AlphaGo Zero, by comparison, has only been programmed with the basic rules of Go. Everything else it learned from scratch. As described in a paper published in Nature today, Zero developed its Go skills by competing against itself. It started with random moves on the board, but every time it won, Zero updated its own system, and played itself again. And again. Millions of times over. After three days of self-play, Zero was strong enough to defeat the version of itself that beat 18-time world champion Lee Se-dol, winning handily -- 100 games to nil. After 40 days, it had a 90 percent win rate against the most advanced version of the original AlphaGo software. DeepMind says this makes it arguably the strongest Go player in history.
I, for one, welcome our new Go-playing robotic overlords.
General Relativity: Space-time tells matter where to go; Matter tells space-time what shape to be.
>> After three days of self-play, Zero was strong enough to defeat >> the version of itself that beat 18-time world champion Lee Se-dol, winning handily -- 100 games to nil. >> After 40 days, it had a 90 percent win rate against the most advanced version of the original AlphaGo software. So after 3 days, it had 100% win rate, after 40 days it had only 90% win rate.
... It can deal with hidden information.
So you mean something like poker? AI beats pros at Texas-Hold'em.
Sorry, I doubt there is any chance that a neural network can be used in a meaningfull way in drug design or climate modeling.
Cost free eBook I read (by iBook/Kobo/Amazon/ObookO/Gutenberg etc.): "The Green Odyssey" by Philip Jose Farmer.
That's just what an AI would say, Mr. Schumann
They could have mined tons of bitcoins instead with that computing power.
Poker is a very bad application of 'AI'. Poker is not a game of human strength, but rather a game of exploiting human weakness. A poker playing computer can be superior to a human simply by virtue of its programmers choosing not to program it to have any tells when it bluffs. A computer raising when it has a four of a kind is indistinguishable from a computer raising when it has high card 10.
This is one of the problems in the AI world. They should have targeted playing as well as the average human. There is minimal benefit in being the absolute best Go player that could exist. Difficult and complicated intelligences have to be far more general than that. There is tremendous value in developing an intelligence comparable to normal humans without need for it to be capable of defeating humans who've dedicated their lives to a single obsession at their own game.
Read the book The Promethean by Owen Stanley whereby a tech billionaire decided to build the perfect AI as a gift to humanity. Social Justice Warrior intervene and taught him the concept of social justice and the AI forked itself to infinity.
Not impressed, doesn't prove anything, and why should anyone even care?
Maybe because they're not trying to prove anything? Maybe their actual goal is to improve general purpose algorithms by an iterative approach? Like it says in the article. Which you read of course.
No, this is part of the effort to find the question.
What side do you want?
If it were all about tells there would be no online poker. Poker IS about reading other players but you can read a player from their play.
Next up, calling people out for begging the question on that whole gravity "theory" by asking them guess where an Apple tossed up in the air will land when it comes down.
Such as Global Warming[TM]?
Oh you're denialist too!
Kind of obvious in hindsignt given the nature of your sig.
I can already see the next generation of Illiberalas taunting skeptics to "win a game of Go against this AI before questioning Climate Change".
Welp, if you've run out of things to be outraged about, just make some up and get outraged about those! Darn librulhs.
SJW n. One who posts facts.
I'm the parent AC.
I know about the poker AI, but the thing is that solution is very non-general.
AlphaGo's inner workings are based on the MCTS algorithm (which is a fairly general algorithm that can be applied to many games). It would be more interesting to see MCTS properly developed to deal with hidden information. There are improvements (ISMCTS, MO-ISMCTS and MT-ISMCTS) but they are all extremely limited in one way or another.
A general solution would be awesome.
That's the skill in writing AI for computer games. You don't just want to have the game always at skill level infinity, you want to have the whole range from skill level 0 to infinity. Skill level 0 is simply making moves at random. Sometimes, it's up to looking ahead a dozen moves, other times, it's not constantly making aggressive moves.
Vintage computer adverts: http://www.vintageadbrowser.com/computers-and-software-ads
I've decided that this accomplishment -- a dizzying milestone in artificial intelligence that not long ago was though impossible or at least decades away -- is actually meaningless and doesn't prove anything and they should clearly have been working on some other problem. I have no idea how their system works, but I'm confident that their approach is just "brute force" (or something, I clearly have no idea what even that means) and won't generalize to any "real" problem solving (with my definition of "real problem" subject to change without notice).
I will only admit that any progress has been made towards artificial intelligence when computers perform exactly equivalent to humans in all tasks with no human intervention. I mean, I won't really, because I have weird quasi-spiritual hangups about believing computers can be intelligent, but that's where I'm putting the goal posts for now. Digital computers can't think, but I can because reasons. Free will or quantum mechanics or something else that I haven't thought about at all, probably.
Also, cotton gins and blacksmiths, therefore computers will never take our jobs. Amen.
Let's not stir that bag of worms...
The way to pass Turing test would be to convert it into creationism as only human would believe it?
Oh wait, this should be easy, it was created...
4wdloop
"programmers choosing not to program it to have any tells "
Actually, creating and exploiting fake tells is exactly how an AI poker can win.
1. A simple computer has 4 of a kind and always goes All-In.
2. An advanced computer has 4 of a kind and sometimes calls and sometimes goes all-in, just to mix it up.
3. A good AI computer has 4 of a kind and has carefully built a fake trail of typically acting a certain way with a good hand, but this time decides to simply bet 18%, which is calculated based on what it has learned of the human players typical traits regarding calling or folding in order to optimize the take.
They should have developed it to *teach* someone to play go, and correctly assess and match their skill level no matter what that may be.
Laws are rules for the court, but merely a bottom bar to hit for life. Think beyond laws in your actions always.
Except being the best at Go says nothing about how it is at health diagnosis or creating more efficient processors. Quite a leap you're taking there.
Laws are rules for the court, but merely a bottom bar to hit for life. Think beyond laws in your actions always.
Although to me this is clearly a case of A.I., parent has a point that not all types of problems lend themselves to be treated as reinforcement learning tasks, which seems to have been the key to success in this case.
Can you make the "game" be the game of learning? I can imagine the dataset would be the rules of many different games and the solutions would be networks that learn those games with solution quality based on some balance of leanness and efficacy of the networks. You'd then let it loose teaching itself how to best teach networks. Hmmm.
Don't be too hard on him, manufacturing this sort of outrage is a time-honored tradition and puts bread on a lot of tables.
Outrage doesn't just grow on trees you know. Without his efforts the outrage deficit would lead to a world awash with harmony.
A general solution would be awesome.
General solutions require Strong AI, which, for now, is science fiction.
Complaining that this self-learning Go program isn't general purpose is sort of like complaining that a better electric car battery won't help your Tesla reach Warp 9.
This is incremental progress, not a revolution, but it is still an interesting advance.
Well the TotallyFake non-AI are coming for your jobs regardless of how impressed you are.
The essence of intelligence is that it enables one to predict the outcome of a unique situation based upon an understanding of its essential elements.
Starting with only the rules of Go, Zero explored a variety of combinations, learning that some were more likely to give a satisfactory result. It developed a sense of what types of moves are best. Thus, without playing or studying an infinite number of games it could know the type of move that should be best in each unique situation.
Theoretically, a vast intelligence, given only the facts of the Big Bang, could anticipate most of the resulting evolution of our universe. Zero has taken the first small step.
...omphaloskepsis often...
Although to me this is clearly a case of A.I., parent has a point that not all types of problems lend themselves to be treated as reinforcement learning tasks, which seems to have been the key to success in this case.
Like? Evolution is a form of reinforcement "learning". And for the nature vs nurture crowd, so is the passing down of knowledge and skills. Even in situations that aren't actually physically repeated we make a mental simulation of what we think might happen and play out scenarios, then it's corrected by reality as things do or don't work out as we thought. Of course a Go AI has it easy in that the rules define the outcome so it can just play itself a zillion times without external input. If you were trying to be say a chef it'd be pretty hard to lock yourself in a kitchen all alone and come out a three star Michelin chef.
Live today, because you never know what tomorrow brings
Boards may be 21x21 but may be other sizes as well.
She was like chocolate when she drank... semi-sweet at first and then increasingly bitter.
Not all task cab be reinforcement learning tasks but all task can be broken down into reinforcement learning subtasks. Your thoughts and deeds are not the singular actions you think they are, they are a composite of many inputs, feeding into many thought recurrent though processes, circulating through many decision trees, and that continuously composite of results, leading to actions to be taken. So each sub task can be learnt, the composition of sub tasks can be learnt, once each sub task has been learnt, well maybe, insufficient sub tasks learnt and the composite will fail. Some sub-tasks can fail if the remaining sub tasks can take over or if a new subtask can be created on the fly and incorporated into the composite.
Chaos - everything, everywhere, everywhen
This doesn't show we are winning at creating AI. It simply shows that the game of Go is more tractable than we previously thought. Claims about the number of positions in Go being vastly greater than the number of atoms in the universe (something like the number of atoms squared) completely miss the point: this is a straw man argument for why algorithms weren't good at Go until recently, since (obviously) humans are not searching the entire space of all possible board positions either. It stands to reason that once a sufficiently flexible fuzzy hierarchical pattern matching algorithm were produced, it would be able to play Go much better than a human.
And that son is how Skynet was born. Damn humans never learn anything.
I have programmed this type of learning algorithm in the past. About 30 years ago when computers were about 30000 times slower than now.
Anyway, you can have the program play itself for a while and it becomes quite good. But you won't know how it will perform against a human unless you try. It might be very good against those moves that the computer player will come up with, but very bad against moves thought up by a human.
30 years ago I tackled a simpler game than go.
There is minimal benefit in being the absolute best Go player that could exist.
This is a research project. People want to know how far they can push it.
Dumbing it down to provide a useful challenge for humans is easy.
To those on slashdot who believe that this limited application truly implies Artificial Intelligence: I challenge this software to make a peanut butter sandwich.
I'm not talking about dumbing it down to provide a useful challenge for humans, I'm talking about having it play Go, compose music, and write poetry. Using AI for anything but a toy application like this will require AI that master many poorly related skills and combine those skills to execute a complex task. If you ever want an AI to write a useful fiction crime novel that AI will not only need to be able to compose English and be creative it will need to be able to research the various topics involved come to a high level understanding of these various unrelated subjects and hypothetically apply them in some unique, plausible, and unsurprising way. You are never going to get there if you are with algorithms designed to waste effort trying to be the best at everything, you need algorithms that look for "just as much as I need" otherwise your book writing AI would spend a lifetime on each of the dozens of different things it needs to research to write the book.
More fake news bullshit machines ain't takin' nobody's JERBS STFU faggot
He posted angrily from the cab of a self-driving semi-truck.
Guys, imagine how ironic this is if the shills have some automation script to shitpost any article covering automation.
Who says it couldn't? You'd need to provide it with robotic actuators, a camera, lots of butter and bread, and a score function describing how sandwich-buttery is any given situation. But it's only fair that you provide a definition of the problem to solve, since a human would need it too.
Under these conditions, it's quite possible that the algorithm would learn how to make sandwiches. They've achieved it with playing video-games, after all.
Singularity: a belief in the "God" idea with the "demiurge" relation inverted.
Let me put this another way since so many seem to think I'm talking about dumbing it down enough to be fun to play. We aren't talking about a game bot we are talking about an AI development project.
Humans are a ridiculously powerful AI's, much more powerful than AlphaGo, so why can it beat us? It isn't because AlphaGo never sleeps, in fact, AlphaGo does sleep it just spreads it among more frequent and smaller time scales. We can't beat it for the same reason we can beat AlphaGo at everything but playing go. From the moment of birth we'd need to be hard-wired into a minimal set of controls with a direct brain interface with all unrelated sensory input shut off, feeding and waste removal processes automatically handled. That is the human equivalent of the AI they are building. Except they are pre-programming it with the rules and objective even though Go has an extremely simple play mechanism.
This approach is only going to get you so far, if it weren't humans would be better go players. Human limitations exist for a reason we don't get bored with performing a single task over and over forever because we suck, we get bored as a mechanism to shift and spread mental resources among more skills which provide alternative insights that can map back as well as be combined to tackle ever more complex super skills. Mastering Go in itself is pretty useless, mastering go alongside a highly realistic warfare simulation on the other hand...
you still don't get that whole "evidence" thing, do you?
as in, tons of evidence of one thing over here , no evidence over there, yet somehow you want to pretend the two possibilities are equally scientifically probable, or even that the one with no evidence is more likely to be true regardless of the complete lack of evidence supporting it.
The guy who said the election was rigged won the presidency with the second-most votes.